PAPER-MACHINE DATA-ANALYSIS AND COMPRESSION USING WAVELETS

This paper describes the analysis of paper machine process data using discrete wavelet transforms. The techniques have been adapted from a g eneral signal analysis theory that has been developed in recent years. It is shown that wavelets are an effective representation for the det ection of basis weight and moisture process variations in noisy data a nd lead to improved estimation and visualization of the machine-direct ion and cross-direction variations. Using simulated data, it has been shown that the new methods produce results superior to conventional in dustrially accepted procedures. Industrial data also have been analyze d, and it is apparent that the method has many desirable characteristi cs. The second main advantage of the method has been to allow signific ant compression of the process data without diminishing the ability to reconstruct accurate and detailed profiles. It has been shown that th e compression method can be embedded into the estimation algorithm, pr oducing excellent results without major expense in computation time. T he ability to reduce data storage requirements is of importance in mil l-wide process-monitoring systems.